The efficient and effective delivery of health care has been substantially improved through the development of newer and better ways of measuring patient parameters and managing patient information. Modern clinical practice frequently makes use of equipment and devices that measure such global parameters as blood pressure, blood-gas levels, temperature, cardiac function, and so forth. Other specialized devices, such as implantable medical devices (IMDs) and drug-delivery devices, automatically control and monitor various physiological functions in accordance with predetermined treatment protocols tailored to the needs of particular patients. Still other devices have been developed to periodically check in with patients and, for example, remind them to take prescriptions, visit their doctors, or otherwise take appropriate therapeutic action. Reliance on such devices frees health care professionals to attend to the needs of other patients and focus their attention on those matters where direct human intervention is still required. Reliance on such devices also improves patient well-being and quality of life by freeing the patients from the need for constant direct care by health professionals.
One adverse consequence of such progress is the problem of how best to manage the disparate and voluminous data often generated by the equipment and devices now available. The modern-day problem of “information overload” is as prevalent and serious in the field of automated remote health care as elsewhere. A need exists not only for better ways of obtaining relevant information regarding patients and their conditions, but for managing and making best use of that information once obtained.
To manage the multitude of data now made available to health care professionals in the course of medical treatment, various systems and methods have been proposed and developed. Conventional patient monitors, for example, sound a warning in the event a measured parameter, such as cardiac activity, falls outside of pre-determined limits. Other devices, such as implantable pacemakers or automatic defibrillators, are programmed to implement therapeutic protocols automatically in the event that certain physiological conditions are detected. Still other devices have been developed to remind patients when to take a prescription or otherwise implement a predetermined treatment protocol. Although effective for their particular functions, such approaches have been largely independent of one another and lack a centralized coordinating facility that consolidates widely disparate data into a cohesive and meaningful whole.
Therefore, a need exists for frequent and near continuous monitoring of patients with implanted and external medical devices and sensors for problems occurring with their device, sensor outputs, and overall health status. For example, an individual parameter that may not, by itself, indicate a problem might well indicate a problem when combined with information from one or more additional sources. In the past, resolving this problem has required substantial involvement and intervention of health care personnel who can interpret the data and take action as appropriate. In the case of patients who might face potentially critical situations, prudence requires the patients remain in health care facilities, even though their actual condition would permit life outside such a facility.